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10 May 2012 Quantitative analysis of breast DCE-MR images based on ICA and an empirical model
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Abstract
DCE-MRI represents an important tool for detecting subtle kinetic changes in breast lesion tissue. Non-masslike breast lesions exhibit an atypical dynamical behavior compared to mass-like lesions and pose a challenge to a computer-aided diagnosis system. Yet the correct diagnosis of these tumors represents an important step towards early prevention. We apply Independent Component Analysis (ICA) on DCE-MRI images to extract kinetic tumor curves. We use a known empirical mathematical model to automatically identify the tumor curves from the ICA result. Filtering out noise, our technique is superior to traditional ROI-based analysis in capturing the kinetic characteristics of the tumor curves. These typical characteristics enable us to nd out the optimal number of independent components for ICA. Another benet of our method is the segmentation of tumor tissue which is superior to the segmentation from MR subtraction images. Our aim is a optimal extraction of tumor curves to provide a better basis for kinetic analysis and to distinguish between benign and malignant lesions, especially for the challenging non-mass-like breast lesions.
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Sebastian Goebl, Claudia Plant, Marc Lobbes M.D., and Anke Meyer-Bäse "Quantitative analysis of breast DCE-MR images based on ICA and an empirical model", Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 84010Z (10 May 2012); https://doi.org/10.1117/12.921952
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